Nvidia Founder, CEO Jensen Huang to Carnegie Mellon University Graduates: ‘Shape What Comes Next’

Insider Brief

  • Jensen Huang told graduates at Carnegie Mellon University they are entering a new industrial era shaped by artificial intelligence, accelerated computing and scientific discovery during the university’s 128th commencement ceremony.
  • Huang, who received an honorary Doctor of Science and Technology degree, described AI as a foundational technology shift that will reshape industries, expand scientific research and accelerate economic and industrial development while urging graduates to approach technological progress with persistence and long-term commitment.
  • Carnegie Mellon University also highlighted its growing robotics and AI programs during the ceremony, including recognition of Beverly Da Costa as the first student to receive the university’s Bachelor of Science degree in Robotics.

Nvidia founder and CEO Jensen Huang told Carnegie Mellon University graduates they are entering a new industrial era shaped by artificial intelligence, accelerated computing and scientific discovery during the university’s 128th commencement ceremony on Sunday.

According to Carnegie Mellon University, the Nvidia chief executive spoke to more than 5,800 undergraduate and graduate students during the ceremony, emphasizing the growing role of AI and computing infrastructure in what he described as a rapidly changing global environment.

“You are entering the world at an extraordinary moment,” Huang said. “A new industry is being born. A new era of science and discovery is beginning. AI will accelerate the expansion of human knowledge and help solve problems once beyond our reach,” he said. “No generation has entered the world with more powerful tools — or greater opportunities — than you. We are all standing at the same starting line. This is your moment to help shape what comes next. So run. Don’t walk.”

Huang, who was given an honorary Doctor of Science and Technology degree from the university, framed AI as a foundational technology shift that will reshape industries, expand scientific research capabilities and accelerate economic and industrial development.

“Carnegie Mellon has a motto I love: My heart is in the work,” he said. “So, put your heart in the work. Build something worthy of your education, your potential, and the people who believed in you long before the world did,” he said. “We have the opportunity to close the technology divide — and bring the power of computing and intelligence to billions of people for the very first time. To reindustrialize America and restore our capacity to build. And to help create a future more abundant, more capable and more hopeful than the world you inherited.”

Huang also urged graduates to approach technological development with persistence and long-term commitment, drawing on lessons from his more than three decades building Nvidia into one of the central infrastructure companies powering modern AI systems.

Prior to the commencement ceremony, Huang met with Carnegie Mellon students and reviewed research projects spanning robotics, AI and engineering disciplines.

Carnegie Mellon President Farnam Jahanian introduced Huang by highlighting Nvidia’s growing influence across research, business and technology development, particularly in areas tied to advanced computing and AI platforms.

“His influence extends far beyond the technology sector, with tools and platforms that are empowering researchers, practitioners, students, creators and entrepreneurs around the globe to tackle increasingly complex challenges and unlock new possibilities,” Jahanian said.

The university noted the ceremony also reflected Carnegie Mellon’s expanding role in robotics and AI education. Among the graduates recognized was Beverly Da Costa, the first student to receive the university’s Bachelor of Science degree in Robotics. Da Costa said her education focused heavily on practical robotics development, including hardware integration, testing and real-world system failures.

“That bakes the memories and lessons into your brain in a way that sticks, especially the mistakes,” she said. “I feel ready for what’s next.”

Image credit: Carnegie Mellon University

The Week Ahead in AI: AI Layoffs, Perplexity Launches Mac AI Agent, Jensen Huang Speaks to SMU Grads, Plus Funding, Upcoming Earnings & AI Week New York

Welcome to AI Insider’s The Week Ahead in AI. See the key developments and events we’re watching May 10- 16.

Weekend AI News Briefs

Oracle’s AI-Driven Mass Layoff of 30,000 Draws Backlash Over Severance Terms and Forfeited Stock

Oracle’s reported March 31 layoff of an estimated 20,000 to 30,000 employees has triggered employee criticism over severance terms, particularly the company’s decision not to accelerate unvested stock grants that some workers said were worth hundreds of thousands of dollars. Former employees also raised concerns about Oracle classifying some hybrid workers as remote employees during the layoffs, while a petition signed by at least 90 workers urged the company to match severance packages previously offered by Meta, Microsoft and Cloudflare during AI-related restructurings. (AI Insider)

Cloudflare Cuts 20% of Workforce Citing AI Productivity Gains as Quarterly Revenue Hits Record $639M

Cloudflare announced its first mass layoff, cutting about 1,100 employees, or roughly 20% of its workforce, as the company reported record quarterly revenue of $639.8 million, up 34% year over year. Company executives said the restructuring reflected growing internal use of AI tools across engineering, HR, finance and marketing operations, with autonomous AI systems increasingly handling coding review and other productivity tasks. (AI Insider)

Davis Closes €4.6M Funding Round to Deploy Proprietary AI Model for Architectural Design Under Regulatory Constraints

Paris-based AI real estate startup Davis raised €4.6 million in a pre-seed round led by Heartcore Capital and Balderton Capital to expand its AI-driven architectural and feasibility planning platform. The company also launched Gaudi-1, a model designed to generate architect-grade floor plans and building layouts under real-world regulatory constraints while combining AI generation with human expert review. (AI Insider)

AI Library Raises Pre-Seed Funding to Automate Enterprise Software Delivery With AI Agents

AI Library, an outcome-based software delivery startup founded in 2023, raised $560,000 in pre-seed funding at a $7.5 million valuation cap to expand its AI agent-driven enterprise software deployment platform. The company also launched an MCP infrastructure layer designed to give coding agents structured access to enterprise tools, data and workflows while reducing fragmented integrations and improving reliability for AI deployments. (AI Insider)

Modicus Prime Announces $8M in Funding to Enable AI Audit Readiness Across Pharma

Modicus Prime raised an additional $4.5 million led by Frist Cressey Ventures, bringing total funding to $8 million, to expand its AI compliance software for pharmaceutical companies operating in regulated manufacturing and quality-control environments. The company said its platform is designed to help drugmakers manage audit-ready AI systems across FDA and EMA regulatory frameworks while integrating with existing pharmaceutical technology and quality management systems. (AI Insider)

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Perplexity Launches Personal Computer AI Agent for All Mac Users to Rival Local AI Assistants

Perplexity opened its Personal Computer feature to all Mac users through a new desktop app that allows AI agents to access local files, native applications, web tools and hundreds of external connectors for multi-step workflows. The company said the system operates through a secure server-based environment and can also be accessed remotely through iPhone integration and the company’s Comet browser platform. (AI Insider)

Nvidia Founder, CEO Jensen Huang to Carnegie Mellon University Graduates: ‘Shape What Comes Next’

Nvidia CEO Jensen Huang told Carnegie Mellon University graduates they are entering a new industrial era shaped by artificial intelligence, accelerated computing and scientific discovery during the university’s 128th commencement ceremony, where he also received an honorary Doctor of Science and Technology degree. Carnegie Mellon awarded more than 5,800 undergraduate and graduate degrees during the ceremony, which also highlighted the university’s growing role in robotics and AI education. (Carnegie Mellon University)

Upcoming Earnings

Dot Ai (DAIC)

Dot Ai is scheduled to report first-quarter 2026 financial results on May 11, after the market close, with management set to host an investor conference call at 4:30 p.m. ET to discuss earnings results, provide a corporate update and answer investor questions. The company develops IoT and AI-based software-as-a-service technology focused on asset intelligence and industrial technology applications. (Nasdaq)

Richtech Robotics (RR)

Richtech Robotics is estimated to report earnings on May 13, with analysts forecasting a quarterly loss of 3 cents per share, according to Zacks Investment Research. The consensus estimate compares with a reported loss of 4 cents per share in the same quarter last year. (Nasdaq)

Alibaba Group Holding (BABA)

Alibaba Group Holding is expected to report fiscal fourth-quarter 2026 earnings on May 13, before the market open, with analysts forecasting earnings of $1.02 per share for the quarter ended March 2026, according to Zacks Investment Research. The consensus estimate compares with reported earnings of $1.57 per share in the same quarter last year. (Nasdaq)

Cisco Systems (CSCO)

Cisco Systems is expected to report fiscal third-quarter 2026 earnings on May 13, after the market close, with analysts forecasting earnings of 86 cents per share for the quarter ended April 2026, according to Zacks Investment Research. The consensus estimate compares with reported earnings of 78 cents per share in the same quarter last year. (Nasdaq)

Upcoming Events

AI Week New York 2026

May 11–17, New York City, a citywide, community-led tech festival organized by Pulse NYC featuring 100+ events, 20,000+ expected attendees, panels, workshops, talks, demos, and networking opportunities across themes including generative AI, enterprise AI implementation, ethical AI, infrastructure, future of work, startups, and social impact. The week includes the flagship Brooklyn Tech Expo on May 12 in Dumbo, Brooklyn’s largest annual tech conference with 1,000+ attendees focused on AI and emerging technologies, seminars, and real-world applications. (Pulse NYC)

SaaStr AI Annual 2026

May 12–14, San Francisco Bay Area (San Mateo), billed as the world’s largest B2B + AI gathering bringing together 10,000+ founders, executives, and VCs for 200+ tactical sessions on deploying AI agents at scale, shipping AI products, building AI-native operating models, monetization, and organizational transformation. Features speakers from OpenAI, Anthropic, Databricks, and other leading companies, plus dedicated summits for revenue, customer, and marketing leaders, extensive networking, and VC matchmaking on an immersive campus. (SaaStr AI Annual 2026)

Gartner Data & Analytics Summit London 2026

May 11–13, London, UK (ExCeL London), a conference for CDAOs, heads of AI, data and analytics leaders, and executives, covering data management, agentic AI, generative AI, governance, architecture, modernization, and delivering measurable business value from data and AI initiatives. It draws thousands of senior leaders for strategy-focused sessions and trends analysis. (Gartner)


Check out AI Insider every day for the latest in artificial intelligence and robotics.

Google DeepMind’s powerful AI co-mathematician

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Good morning, {{ first_name | AI enthusiasts }}. Google DeepMind just took AI’s coding strategy and applied it to math: don’t ask a model for the answer, give a team of agents the workspace.

The company’s AI co-mathematician just scored a new high on a benchmark built to stump AI for decades, with one professor even cracking an unsolved problem using a strategy buried inside a proof the system’s own reviewers had rejected.


In today’s AI rundown:

  • Google DeepMind’s AI co-mathematician

  • The Rundown Roundtable: Our AI use cases

  • Automate any manual task with Codex

  • AI finds 100+ new exoplanets from NASA data

  • 4 new AI tools, community workflows, and more

LATEST DEVELOPMENTS

GOOGLE DEEPMIND

🧮 Google DeepMind’s AI co-mathematician

Image source: Pushmeet Kohli (@pushmeet on X)

The Rundown: Google DeepMind just published a paper on its AI co-mathematician, an agentic system based on Gemini 3.1 built to help mathematicians tackle unsolved problems — setting a new high on a benchmark of research-level math problems.

The details:

  • DeepMind modeled the tool after AI coding environments like Claude Code, bringing agent teams and built-in review cycles to math research.

  • A coordinator agent breaks research into parallel workstreams, each with sub-agents that write code, search literature, and attempt proofs.

  • Oxford’s Marc Lackenby resolved an open problem in the Kourovka Notebook after spotting a ‘really, really clever proof strategy’ inside a rejected output.

  • On Epoch AI’s FrontierMath Tier 4, the system topped the leaderboard at 48% and more than doubled Gemini 3.1 Pro’s 19% raw score.

Why it matters: AI has already led to a surge in mathematics discoveries with the advances in frontier models, and similar to coding, agentic pipelines are now enabling AI systems to push even further. But as Lackenby’s discovery shows, the future is still bright for AI that enables top minds to accelerate their work, not replace it.

TOGETHER WITH GOOGLE FOR STARTUPS

📚 Master generative media for startups

The Rundown: Google for Startups’ Future of AI report is your essential guide to understanding how generative media is reshaping product development, offering founders strategic insights to build smarter, scale faster, and stay ahead of the AI curve.

Inside the report, you’ll discover:

  • How to leverage digital twinning at scale.

  • Strategic insights for AI product differentiation.

  • Expert perspectives on the generative landscape.

Download the report today.

THE RUNDOWN ROUNDTABLE

💡 The Rundown Roundtable: Our AI use cases

Image source: Ideogram / The Rundown

The Rundown: The Rundown Roundtable is a weekly feature where we poll members of The Rundown staff about how we use AI in our work or daily lives.

Jason, Developer: I used /goal in OpenAI’s Codex to build a Magic: The Gathering app so my brother and I can play asynchronously without needing to coordinate a call or awkwardly play over FaceTime.

The idea is to let each of us take turns when we have time, track the board state cleanly, and keep a game going over days instead of trying to line up schedules. The command allowed Codex to continue running until everything was done, basically one-shotting exactly what I was looking for without any intervention.

Joey, Partnerships: I’ve never been to Greece, so for my upcoming trip, I went all in and handed the whole itinerary over to Claude. Flights booked, transit times dialed, restaurant lists curated city by city.

I’m now showing up with a plan tighter than most travel agents could put together!

AI TRAINING

✅ Automate any manual task with Codex

The Rundown: In this guide, you will learn how to let Codex click through any annoying, repetitive work using Computer Use on Mac or Windows.

Step-by-step:

  1. Open Codex, go to Plugins, find and enable the Computer Use plugin, and start a new task

  2. Open the permissions menu and switch from Default permissions to Full access, then confirm any prompts and give Codex something real to do

  3. Example: “Open Chrome and debug this web page UI I’m developing http://localhost:3000/. Click through, reproduce the bug I describe, then tell me what you think is causing it. If not sure, ask before making changes”

Pro tip: Codex can automate repetitive workflows in local apps, too — try it for Photoshop exports, Adobe Premiere cleanup, file renaming, or any other tool.

PRESENTED BY ORACLE DEVELOPERS

🚀 Small models, bigger reasoning

The Rundown: Small language models can solve harder reasoning tasks without changing their weights. Oracle Developers’ open-source agent-reasoning code shows how to add research-backed orchestration to Ollama models, with 16 reasoning strategies developers can test locally.

In the guide, you’ll explore:

  • Open-source reasoning code for Ollama

  • 16 strategies, benchmarked across 4,200 runs

  • Better accuracy without retraining models

Get the open-source reasoning patterns. Explore the guide.

AI & ASTRONOMY

🪐 AI finds 100+ new exoplanets from NASA data

Image source: NASA

The Rundown: University of Warwick astronomers confirmed more than 100 exoplanets using an AI system called RAVEN that scanned 4 years of NASA TESS data covering 2.2M stars, with RAVEN also finding 2,000+ additional potential candidates.

The details:

  • RAVEN handles detection, vetting, and confirmation in one shot, trained on simulated planets and false-alarm signals to filter real finds.

  • The findings included 31 exoplanets never before spotted, plus strange worlds that orbit around their stars in under a day.

  • Hundreds of exoplanets were found in the “Neptunian Desert”, a region so close to a star that Neptune-sized planets shouldn’t survive the heat.

  • The system measures how common different planet types are at 10x the precision of previous systems from smarter AI alone, not new hardware.

Why it matters: Humans have confirmed just a few thousand exoplanets so far, and there are estimated to be trillions. AI and tech advances are going to help rewrite that number fast — and judging from RAVEN, all it will take is upgraded models and AI integrations to uncover knowledge about space already hiding in the data we have.

QUICK HITS

🛠️ Trending AI Tools

  • 🔒 Incogni – Remove your personal data from the web so scammers and identity thieves can’t access it. Use code RUNDOWN to get 55% off*

  • 💻 Codex in Chrome – OAI’s Codex extension for agentic tasks inside Chrome

  • 🧠 ERNIE 5.1 – Baidu’s new foundation model with strong search capabilities

  • 🖨️ Printing Press – CLI factory with 30+ pre-built, agent-native tools

*Sponsored Listing

📰 Everything else in AI today

Google’s Isomorphic Labs is reportedly raising $2B+ to expand its Drug Design Engine, which it says significantly outperforms AlphaFold 3 on specific tasks.

Greece is proposing AI protections into its constitution, requiring the tech to serve individual freedom, with PM Mitsotakis citing threats to democracy.

Baidu released ERNIE 5.1, a new AI ranking No. 4 on Arena’s Search Leaderboard, with the company claiming it cost just 6% as much to train as rival models.

OpenRouter launched Pareto Code, a free routing layer that auto-picks the cheapest coding AI above a user-set quality bar, with prices adjusting as newer models improve.

SoftBank Group’s telecom arm launched a battery business to build large-scale cells and storage systems — and meet the power demands of data centers in development.

COMMUNITY

🤝 Community AI workflows

Every newsletter, we showcase how a reader is using AI to work smarter, save time, or make life easier.

Today’s workflow comes from reader Anonymous:

“I have been using ChatGPT for various things professionally, which has been surprisingly helpful and refreshing. The greatest use I have found for it so far, though, is helping me train my 4 dogs.

I was ready to drop thousands of dollars on a professional trainer just because of how chaotic it has been, but ChatGPT has helped me identify the root causes of specific behaviors and taught me how to successfully train around and beyond them using specific techniques tailored to my individual dogs.

The confidence it has brought me, and the positive reinforcements have changed every dynamic in the household, and I wish I had started sooner.”

How do you use AI? Tell us here.

🎓 Highlights: News, Guides & Events

  • Read our last AI newsletter: OpenAI closes reasoning gap in voice agents

  • Read our last Tech newsletter: ‘RAMageddon’ is coming for your laptop

  • Read our last Robotics newsletter: Genesis robot makes breakfast

  • Today’s AI tool guide: Automate any manual task with Codex

See you soon,

Rowan, Joey, Zach, Shubham, and Jennifer — the humans behind The Rundown

AI automates HR compliance, except for the area tech companies need

Artificial intelligence is transforming how companies handle compliance. Background checks run in real-time. Payroll monitoring flags discrepancies automatically. Predictive analytics anticipate employee churn before it happens. HR tech stacks now offer automated solutions for nearly every regulatory requirement – from GDPR data requests to workplace safety reporting.

But there is one glaring exception. For UK tech companies whose competitive advantage depends on hiring international AI talent, the compliance function that matters most remains stubbornly analogue: sponsor licence management.

This creates a dangerous paradox. The sector building the most sophisticated automation tools cannot automate its own immigration compliance. And the consequences are not theoretical. They are immediate and increasingly common – for both employers and the skilled workers who depend on them.

The irony tech founders don’t see coming

Walk into any London tech scaleup and you will find teams building compliance automation. One might be developing AI-powered contract review. Another could be creating real-time financial reporting dashboards. A third might be launching automated cybersecurity monitoring.

These same companies then handle their sponsor licence obligations using spreadsheets, email reminders, and institutional memory. The gap is striking – and it stems from a structural reality most founders do not anticipate.

The Home Office Sponsor Management System was not designed for API integration. Compliance data lives in PDFs and manual entries, not structured databases. Material changes to sponsored workers’ circumstances – the kind of events that trigger reporting obligations – require human judgement to identify and interpret. When a machine learning engineer’s role evolves from individual contributor to team lead, no algorithm flags that this constitutes a “material change in job duties” requiring notification in 10 working days.

The result: tech companies accustomed to automating risk out of their operations are managing sponsor compliance the same way businesses did in 2010. Manually. Inconsistently. And often incorrectly.

For a sector where 30% to 40% of the workforce holds Skilled Worker visas, this is not a minor process inefficiency. It is a systemic operational risk sitting in the least automated corner of the business.

The real stakes for UK tech – and the workers caught in the middle

The numbers tell the story clearly. Between July 2024 and June 2025, 1,948 sponsor licences were revoked in the UK – more than double the previous year. Analysis of Home Office enforcement data shows the tech sector is disproportionately represented in these revocations, not because tech companies are more reckless, but because they are structurally more vulnerable.

AI and machine learning roles are among the hardest to fill domestically. The talent pipeline for specialists in natural language processing, computer vision, and reinforcement learning remains heavily international. A Cambridge-based AI startup competing for Series B funding cannot wait six months to fill a senior ML engineer role with a domestic candidate who may not exist. They hire the best person globally and sponsor them.

This dependency creates exposure. When a sponsor licence is suspended, all sponsored workers’ visas are curtailed to 60 days. For a scaleup with 15 AI engineers on Skilled Worker visas, that is not a staffing adjustment – it is an existential threat to product timelines, investor confidence, and competitive positioning.

But the human cost runs deeper. A skilled worker who relocated their family to the UK, enrolled children in schools, signed a two-year lease – they suddenly have 60 days to secure a new sponsor or leave the country. Their career trajectory, their children’s education, their financial stability all hinge on finding an employer willing to transfer sponsorship in a two-month window.

The financial impact extends beyond direct replacement costs. One mid-sized London fintech lost its licence after a compliance visit uncovered unreported changes in multiple sponsored workers. Eight engineers left in the 60-day window. Three went to competitors. Two returned home. The company faced a 12-month prohibition on applying for a new licence. Eighteen months later, they still had not fully rebuilt their machine learning team. The Series B round they were planning never materialised.

“The businesses facing enforcement action are rarely the ones cutting corners deliberately,” says Yash Dubal, director at A Y & J Solicitors, which advises on Skilled Worker Visa applications and compliance. “They are organisations that built a workforce carefully, sponsored overseas workers through the proper channels, and then – somewhere in the day-to-day pressure of running a business – allowed the ongoing compliance framework to drift.”

At A Y & J Solicitors, which helps professionals and businesses navigate the Skilled Worker Visa route, this pattern emerges repeatedly. Tech companies treat immigration compliance as an HR administrative task not what it actually is: a business-critical governance function sitting at the intersection of talent strategy, regulatory risk, and operational continuity.

The irony is that the solution requires exactly the kind of thinking tech companies excel at – just applied to an unfamiliar domain.

What tech founders consistently miss

The failure mode is predictable. It starts with assumptions that do not hold.

Assumption one: Compliance is like other HR functions. It is not. Payroll errors can be corrected. Missed performance reviews have no regulatory consequence. Sponsor licence breaches trigger enforcement action. There is no grace period, no software patch, no “we’ll fix it in the next sprint.” The Home Office does not operate on agile principles.

Assumption two: There must be a software solution. There is not. The market has produced sophisticated tools for nearly every other compliance challenge, but sponsor licence management remains resistant to full automation because the Home Office systems themselves are not built for it. The regulatory framework pre-dates API-first architecture by decades.

Assumption three: Complexity is overstated. It is not. A material change in a sponsored worker’s circumstances must be reported in 10 working days. What constitutes “material”? A salary increase that pushes total compensation above the original Certificate of Sponsorship amount. A change in job title. A change in working location. A change in working pattern that alters the nature of the role. All of these require human judgement to identify in real-time in a fast-moving organisation.

Assumption four: Our people know what to do. They do not – not without systems. When an AI engineer gets promoted to lead a team, does the engineering manager know this triggers a reporting obligation? Does the HR business partner? Does payroll? In most tech companies, the answer is no. The knowledge exists somewhere, usually in the head of one person who joined three years ago and remembers the licence application process. That is not a system. It is a single point of failure.

“I have sat with clients who believed they were fully compliant, received an inspection, and discovered that what they thought was minor administrative imprecision was, in the Home Office’s view, a pattern of systemic non-compliance,” Dubal explains. “The gap between those two interpretations is where licences are lost – and where skilled workers’ lives are upended.”

The companies that navigate sponsor compliance successfully are not necessarily better resourced. What differentiates them is that they have applied engineering discipline to a legal obligation. They have built systems.

The systems thinking solution

Treating sponsor compliance like an engineering problem changes how it gets managed.

First, define the system boundaries. What events trigger reporting obligations? Job title changes. Salary adjustments above thresholds. Role responsibility shifts. Working location changes. Absences exceeding defined periods. Each is a signal that must be captured and acted on.

Second, create forcing functions. In software development, automated tests prevent broken code from reaching production. The sponsor compliance equivalent is integrating checks into existing workflows. When HR processes a promotion, the system prompts: “Does this person hold a Skilled Worker visa? If yes, review reporting obligations.” When payroll processes a salary increase, the same check occurs. The compliance step is embedded, not optional.

Third, establish verification loops. Quarterly internal audits replicating what a Home Office inspector would examine. Payroll records cross-referenced against Sponsor Management System entries. Employment contracts checked against actual job duties. The gaps surface before an inspector finds them.

Fourth, assign clear ownership. In tech companies, product quality has an owner. Security has an owner. Sponsor licence compliance needs the same governance structure – a named individual with authority and board visibility. Not as an add-on to someone’s existing role, but as a function with defined responsibility.

Fifth, document everything. If the process for reporting a material change exists only in one person’s understanding of “how we do things,” it will fail the moment that person is unavailable. Documentation creates institutional resilience. It allows the process to work the same way regardless of who is executing it.

This is not revolutionary thinking for tech companies. It is how they already manage code deployments, infrastructure changes, and data governance. The challenge is recognising that sponsor compliance deserves the same operational rigour.

The questions every tech board should ask

The paradox remains: the sector most capable of building automated compliance systems cannot yet automate its most critical compliance function. But tech founders are problem solvers. The path forward requires asking three questions:

Redundancy: If our Head of HR left tomorrow, does the step-by-step process for a “Change of Circumstance” report exist in a shared manual, or is it in their head?

Integration: Is our immigration lawyer a firefighter we call when things go wrong, or are they an architect helping us build these internal checks?

Visibility: Does the Board understand that a simple 11-day delay in reporting a salary bump could technically trigger a 60-day countdown for 40% of our engineering staff?

The answers reveal whether sponsor compliance is treated as a system or as tribal knowledge. In a sector built on eliminating single points of failure, that distinction matters – not for the business, but for every skilled worker whose UK future depends on getting it right.

The post AI automates HR compliance, except for the area tech companies need appeared first on AI News.

Bain sees US$100 billion SaaS market in agentic AI automation

Bain & Company has estimated a US$100 billion market in the US for SaaS companies using agentic AI. The firm said the market is tied to automating coordination work in enterprise systems.

The estimate comes from the second report in Bain’s five-part series on the software industry in the age of AI. The report examines where agentic AI could create new software markets and how SaaS companies can capture them.

Coordination work in enterprise systems

Bain said the market lies in the manual work employees perform between enterprise applications. These workflows often span ERP, CRM and support systems. They may also involve vendor management tools and email.

That work includes pulling data from one system and checking it against another source. It can also involve interpreting unstructured messages and deciding whether to approve, respond, escalate, or wait.

Bain said rules-based automation and robotic process automation are limited in workflows involving ambiguity and information spread in multiple systems. Agentic AI can interpret information from different sources, coordinate actions in systems, and operate in policy guardrails.

The report argues that agentic AI is not primarily a replacement for SaaS platforms, but that the market comes from converting labour-intensive coordination work into software spending.

It estimates vendors are already capturing US$4 billion to US$6 billion of the US market. More than 90% remains untapped, according to the firm.

Outside the US, Bain estimated that Canada, Europe, Australia, and New Zealand could add a similar-sized market. That would bring the total in those regions and the US to about US$200 billion.

Market size by function

The market is not evenly distributed in enterprise functions. Bain estimates that sales represents the largest single share at about US$20 billion. This is mainly due to the number of sales employees, not unusually high automation potential.

Cost of goods sold and operations account for about US$26 billion. The large size of the operational workforce means even modest automation rates can translate into a large addressable market. R&D and engineering, customer support, and finance each represent about US$6 billion to US$12 billion in addressable market size. These functions have sizeable workforces and higher automation potential in specific workflows.

Customer support and R&D or engineering have the highest automation potential, with roughly 40% to 60% of workflow tasks automatable. Bain said both areas have structured data, standardised processes, and clearer output signals. Finance and human resources fall in the 35% to 45% range. The report said accounts payable and payroll have higher automation potential, while financial planning and employee relations involve more judgement.

Sales and IT sit at 30% to 40%. Bain pointed to relationship nuance, deal-by-deal variation, and the unpredictable nature of security incidents as limits on automation in those areas. Legal has lower overall automation potential, at 20% to 30%. Bain said contract review and compliance are repeatable, but the consequences of errors create a need for tighter oversight.

Bain’s automation factors

The report identifies six factors that determine how much of a workflow can realistically be handled by an AI agent. They include output verifiability, consequence of failure, digitised knowledge availability, and process variability. Bain said workflows with clear verification signals are easier to automate than work involving subjective judgement. Examples include compiling code, reconciled invoices, and resolved support tickets.

Workflows involving regulatory or financial risk require closer human supervision, even where agents are technically capable, according to the report. These include tax filings, legal compliance, and security incident response.

Bain also identified digitised knowledge availability as a constraint. Agents need access to structured data and documented context. They also need machine-readable inputs, including decision logic that often sits informally with experienced employees.

Integration complexity affects automation when workflows pass through several systems and APIs. Authentication layers and exception-handling processes add further complexity, and these workflows are harder to automate end-to-end than workflows contained in a single platform. The highest-value areas are concentrated where no single system of record controls the full outcome. These workflows often span ERP, CRM and support systems, the company says.

David Crawford, chairman of Bain’s global technology and telecommunications practice, said SaaS companies have spent the past two decades building positions around systems of record with the next source of advantage being “cross-workflow decision context,” which is defined as the ability to interpret and act in workflows that move through multiple systems.

Company examples and adjacent workflows

The report cited Cursor, Sierra, Harvey, Glean, Salesforce, ServiceNow, and Workday in its discussion of agentic AI adoption. Cursor has surpassed US$16.7 million in average monthly revenue, according to Bain, after doubling in a single quarter. Sierra has crossed US$150 million per annum, Harvey passed US$190 million pa, and Glean US$200 million pa.

The report also pointed to GitHub for example of a company using data from an existing core workflow to move into adjacent work. GitHub’s core business is developer collaboration and source control, but its repository and workflow data helped support expansion into AI-assisted developer productivity and security automation.

Bain said SaaS companies can expand through two types of workflow automation. The first is automating core workflows, where they already have domain knowledge and customer trust. Bain said existing system integrations can support automation of core workflows. The second is automating adjacent workflows that the company does not currently serve directly. These areas can be harder to identify because they require detailed mapping of customer workflows and the underlying data that supports decisions.

Pricing models can change when agents deliver completed outcomes. Bain said outcome- and use-based pricing can become more relevant when agents resolve issues or process invoices. The report contrasts this with traditional pricing based on seats and logins.

Bain’s recommendations for SaaS companies

Bain recommended that SaaS companies begin by identifying which customer workflows are now automatable with agentic AI. The firm said companies should assess automation at the subprocess level not treating entire functions as equally automatable.

The report also said companies should assess the quality of their data. Bain said relevant factors include whether the data is comprehensive, tied to outcomes, and usable for automation.

Bain said companies could close ability gaps through internal development, acquisitions, or partnerships. The report cited AppLovin’s in-house development of its Axon platform, ServiceNow’s acquisition of Moveworks, and Salesforce’s partnership with Workday as examples of different approaches.

The firm also pointed to the need for AI engineering talent, cloud-native architecture for multi-agent orchestration, and funding for model training and inference. It said companies should align pricing and sales incentives with AI-driven outcomes not legacy seat-based models.

Bain said SaaS companies will also need data and product foundations designed for agentic workflows, including machine-readable hand-offs and systems that capture decisions and outcomes from each workflow run.

Crawford said the timeframe for SaaS companies is “measured in quarters, not years,” as AI-native companies gather more deployment data with each customer workflow they automate.

(Photo by engin akyurt)

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